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VERSION:2.0
PRODID:Linklings LLC
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TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135126Z
LOCATION:Riverside East
DTSTART;TZID=America/Chicago:20251014T173000
DTEND;TZID=America/Chicago:20251014T183000
UID:HFESAM_ASPIRE 2025_sess131_POST370@linklings.com
SUMMARY:Mental Workload Classification Using Electrocardiogram Data
DESCRIPTION:Mohsen Behradfar and Dr. Joseph Nuamah (Oklahoma State Univers
 ity)\n\nMental workload is a critical factor in human performance, particu
 larly in high-stakes environments like aviation and healthcare. Excessive 
 cognitive demands can impair decision-making, leading to safety risks. Thi
 s study investigates the use of ECG-derived features for classifying menta
 l workload levels using machine learning models. ECG data was collected fr
 om participants engaged in a virtual flight task with varying workload con
 ditions. We applied five machine learning models—Logistic Regression, Rand
 om Forest, Support Vector Machine, Extreme Gradient Boosting, and Gradient
  Boosting—to classify mental workload in a binary framework (easiest vs. m
 ost difficult levels). Results show that RF outperformed the others, achie
 ving the highest F1 score of 66.7%. These findings highlight the potential
  of ECG-based classification for real-time mental workload monitoring, wit
 h implications for improving safety and performance in critical applicatio
 ns. Further exploration of multimodal approaches could enhance classificat
 ion accuracy and robustness.\n\n
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